Date of Submission
Spring 5-10-2018
Degree Type
Thesis
Degree Name
Master of Science in Computer Science (MSCS)
Department
Computer Science
Committee Chair/First Advisor
Dan Lo
Track
CyberSecurity
Chair
Dan Lo
Committee Member
Dan Lo
Committee Member
Mingon Kang
Committee Member
Qian Kai
Abstract
Malware classification is a critical part in the cybersecurity.
Traditional methodologies for the malware classification
typically use static analysis and dynamic analysis to identify malware.
In this paper, a malware classification methodology based
on its binary image and extracting local binary pattern (LBP)
features are proposed. First, malware images are reorganized into
3 by 3 grids which is mainly used to extract LBP feature. Second,
the LBP is implemented on the malware images to extract features
in that it is useful in pattern or texture classification. Finally,
Tensorflow, a library for machine learning, is applied to classify
malware images with the LBP feature. Performance comparison
results among different classifiers with different image descriptors
such as GIST, a spatial envelope, and the LBP demonstrate that
our proposed approach outperforms others.